Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Stratified Sampling Method01:16

Stratified Sampling Method

16.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
16.0K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.3K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.3K
Cluster Sampling Method01:20

Cluster Sampling Method

15.4K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Urban soil <i>Gammaproteobacteria</i> diversity, shaped by biodiverse land use, predicts a lower risk of developing allergy in children.

Urban forestry & urban greening·2026
Same author

Dark diversity framework reconciles Darwin's naturalization conundrum for freshwater fish invasions.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Trait-Dependent Time Lags Amid Global Change in Marine Observed and Dark Diversity on the Dogger Bank (North Sea).

Global change biology·2026
Same author

Rapid adaptation and extinction in synchronized outdoor evolution experiments of <i>Arabidopsis</i>.

Science (New York, N.Y.)·2026
Same author

Growth form and lifespan of herbaceous species mediate the role of traits in short-term drought response.

Nature ecology & evolution·2026
Same author

Intense solar radiation constrains plant species richness in global grasslands.

Proceedings of the National Academy of Sciences of the United States of America·2026

Related Experiment Video

Updated: Mar 14, 2026

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.9K

Large-scale dark diversity estimates: new perspectives with combined methods.

Argo Ronk1, Francesco de Bello2, Pavel Fibich2

  • 1Institute of Ecology and Earth Sciences University of Tartu Lai 40 Tartu 51005 Estonia.

Ecology and Evolution
|September 21, 2016
PubMed
Summary

Understanding dark diversity, the absent yet suitable species, enhances biodiversity studies. Comparing species co-occurrence and distribution modeling methods reveals new ecological patterns, especially in Southern Europe.

Keywords:
Biomodcomposite dark diversityconsensus dark diversityco‐occurrencefrequencyprevalencespecies distribution modeling

More Related Videos

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.9K
Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

8.1K

Related Experiment Videos

Last Updated: Mar 14, 2026

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.9K
Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.9K
Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

8.1K

Area of Science:

  • Ecology
  • Biodiversity Research
  • Biogeography

Background:

  • Large-scale biodiversity assessments benefit from including dark diversity (absent but suitable species).
  • Current dark diversity methodologies require comparative analysis to refine estimation techniques.
  • Understanding the full spectrum of species assemblages is crucial for accurate ecological assessments.

Purpose of the Study:

  • To compare two distinct mathematical methods for estimating dark diversity: species co-occurrence (SCO) and species distribution modeling (SDM).
  • To investigate dark diversity patterns across European and regional scales using high-resolution plant distribution data.
  • To explore the relationship between dark diversity, observed diversity, and environmental factors.

Main Methods:

  • Utilized plant distribution data from the Atlas Florae Europaeae and seven European regions at 50x50 km and 10x10 km grid scales, respectively.
  • Estimated dark diversity using both SCO and SDM approaches for comparative analysis.
  • Analyzed the relationship between dark diversity size and species composition overlap, and tested overlap probability against hypergeometric distribution.

Main Results:

  • Both SCO and SDM methods yielded comparable dark diversity estimates and spatial patterns, with higher dark diversity observed in Southern Europe.
  • A significant overlap (approx. 75%) was found between the species composition identified by the two methods, exceeding chance expectations.
  • Consensus and composite dark diversity estimates displayed consistent distribution patterns.
  • Dark diversity and site diversity completeness showed different relationships with natural and anthropogenic factors compared to observed richness.

Conclusions:

  • Dark diversity analysis reveals significant biodiversity patterns not apparent from observed richness alone.
  • Combining SCO and SDM methods provides a more robust estimation of dark diversity.
  • The study highlights the importance of considering absent species in ecological assessments and reveals distinct biogeographic patterns for dark diversity.